Bayesian inference of the fully specified subdistribution model for survival data with competing risks

Ge, Miaomiao; Chen, Ming‐Hui

doi:10.1007/s10985-012-9221-9

Cited by 15 publications

(14 citation statements)

References 34 publications

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“…45 It is concluded that Bayesian counterpart in competing risk setting is more informative than the conventional methods. 29,[46][47][48] There are some statistical compatible to handle the competing risk with the conventional method. 42,49 The dedicated R packages like mstate and timereg are suitable to work with competing risk in the simpler way.…”

Section: Discussionmentioning

confidence: 99%

A joint longitudinal and survival model for dynamic treatment regimes in Presence of Competing Risk Analysis

Bhattacharjee

2019

Clinical Epidemiology and Global Health

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Background: In oncology setup, the personalized medicine with the Dynamic Treatment Regime(DTR) is the attractive tool for treatment management. It is based on sequence rule by changing the required treatment by looking into patients dynamic condition repeatedly. This repeatedly measured condition generates the longitudinal data and the time-to-event data jointly. The time-to-event data is generated with competing risks. Now handling the joint longitudinal and survival data with competing risks is itself challenging. It becomes more challenging to decide the best effective treatment strategy while we work with DTR approach in presences of competing risks through joint longitudinal and survival model. Methods: This article is dedicated towards development of statistical methodology to handle competing risk analysis for DTR in repeatedly measured survival data. In this study a simulated dataset is used to resemble the observed data distribution seen in a motivating cancer trial data. Results: This algorithm is prepared through Bayesian analysis to obtain the best effective therapeutic regimen. The OpenBUGS function is prepared, which provides the therapeutic effect comparison and there after estimations between different treatment sequences. Conclusion: This developed method is easy to handle for personalized medicine context in oncology for supportive decision rule.

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Section: Discussionmentioning

confidence: 99%

A joint longitudinal and survival model for dynamic treatment regimes in Presence of Competing Risk Analysis

Bhattacharjee

2019

Clinical Epidemiology and Global Health

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“…These two priors would lead to similar posterior estimates if lim || β ||→∞ | I ( β )|/| A i 0 | = c , where c is a constant. We also envision extending the Jeffreys-type prior to other models for competing risks data (for recent contributions and a literature overview, see Ge and Chen 2012; Beyersmann and Scheike 2013; Chen et al 2013; Fine and Lindqvist 2014). …”

Section: Discussionmentioning

confidence: 99%

“…A generalization of the Cox model is the cause-specific hazards model, which was discussed in Gaynor et al (1993) and Ge and Chen (2012). For j = 1, …, J , the cause-specific hazard function for cause j is defined by h Cj ( t ) = lim Δ t →0 Pr( t ≤ T 〈 t + Δ t , δ = j | T ≥ t )/Δ t .…”

Section: Monotone Partial Likelihood and Posterior Proprietymentioning

confidence: 99%

Assessing covariate effects using Jeffreys-type prior in the Cox model in the presence of a monotone partial likelihood

Castro

Schifano

et al. 2017

Journal of Statistical Theory and Practice

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In medical studies, the monotone partial likelihood is frequently encountered in the analysis of time-to-event data using the Cox model. For example, with a binary covariate, the subjects can be classified into two groups. If the event of interest does not occur (zero event) for all the subjects in one of the groups, the resulting partial likelihood is monotone and consequently, the covariate effects are difficult to estimate. In this article, we develop both Bayesian and frequentist approaches using a data-dependent Jeffreys-type prior to handle the monotone partial likelihood problem. We first carry out an in-depth examination of the conditions of the monotone partial likelihood and then characterize sufficient and necessary conditions for the propriety of the Jeffreys-type prior. We further study several theoretical properties of the Jeffreys-type prior for the Cox model. In addition, we propose two variations of the Jeffreys-type prior: the shifted Jeffreys-type prior and the Jeffreys-type prior based on the first risk set. An efficient Markov-chain Monte Carlo algorithm is developed to carry out posterior computation. We perform extensive simulations to examine the performance of parameter estimates and demonstrate the applicability of the proposed method by analyzing real data from the SEER prostate cancer study.

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“…In the case of different causes of death it is only possible to report the first event to occur 60 . There are different approaches for competing risk models: multivariate time to failure model, the cause‐specific hazards model, the mixture model, the subdistribution model, and the full specified subdistribution model 61 . We will only consider here the cause‐specific hazards model, possibly the most popular of them.…”

Section: Competing Risks Modelsmentioning

confidence: 99%

Bayesian survival analysis with BUGS

Álvares

Lázaro

Gómez‐Rubio

et al. 2021

Statistics in Medicine

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Survival analysis is one of the most important fields of statistics in medicine and biological sciences. In addition, the computational advances in the last decades have favored the use of Bayesian methods in this context, providing a flexible and powerful alternative to the traditional frequentist approach. The objective of this article is to summarize some of the most popular Bayesian survival models, such as accelerated failure time, proportional hazards, mixture cure, competing risks, multi‐state, frailty, and joint models of longitudinal and survival data. Moreover, an implementation of each presented model is provided using a BUGS syntax that can be run with JAGS from the R programming language. Reference to other Bayesian R‐packages is also discussed.

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Bayesian inference of the fully specified subdistribution model for survival data with competing risks

Cited by 15 publications

References 34 publications

A joint longitudinal and survival model for dynamic treatment regimes in Presence of Competing Risk Analysis

A joint longitudinal and survival model for dynamic treatment regimes in Presence of Competing Risk Analysis

Assessing covariate effects using Jeffreys-type prior in the Cox model in the presence of a monotone partial likelihood

Bayesian survival analysis with BUGS

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